Figure REF  shows our proposed neural architecture. The backbone of the model consists of two branches - one for extracting information from global features and one for creating a compact representation of spatial features. A multi-layer perceptron (MLP) encoder, consisting of 3 linear layers with rectified linear units (ReLU), is used to learn a representation of the global features with a dimension of \(\mathbb {R}^{256}\) . Similarly, a convolutional neural network (CNN) encoder with 3 Conv2D layers followed a max pooling layer and ReLU activation is used to learn a compact representation of the spatial features. These representations of game states are then fed to a transformer encoder together with the positional encoding that preserves the ordering information. Following the discussion of [1]} and [2]}, we adopt the fixed positional encodings since the transformer model is permutation invariant.
